As correctly outlined in Li et al. [1]}, the EEG data filtering procedure was unclear in [2]}. We start our analysis on the block-design data by evaluating classification performance when using different EEG frequency ranges. Indeed, the cognitive neuroscience community usually employs five general frequency bands, each associated with a variety of brain functions: delta (0-4 Hz), theta (4-8 Hz), alpha (8-16 Hz), beta (16-32 Hz) and gamma (32 Hz to 95 Hz; gamma band activity can be further split into low- and high-frequency gamma with 55 Hz as cut-off). We discard delta waves as these are typically associated to the sleep phase.
Thus, we first compute the classification performance at the remaining five frequency bands, and the results are given in Table REF .
In all cases, classification performance is lower than in [2]}, which reported an average accuracy of about 80%. As outlined in our recent work [4]}, the lower accuracy is due to the removal of EEG drift, because the previous work inadvertently utilized unfiltered EEG data.
With correctly filtered data and using high-frequency gamma band activity, [2]} achieves about 20% classification accuracy;  and  reach about 30% accuracy; our recent approach (i.e., EEG-ChannelNet [4]}) obtains about 50% accuracy (chance is 2.5%).
Given the timescale of high-frequency gamma, it is unlikely that this result is contaminated by a temporal correlation as the activity takes place on a far shorter timescale than low-frequency activity (i.e., it goes through 55-95 cycles a second, resulting in short-lived temporal correlation).
The higher visual object classification at high gamma frequencies is consistent with the cognitive neuroscience literature related to attention, working memory and perceptual processes involved in visual tasks , , . Based on Table REF , in our following analyses we employ the 55-95 Hz frequency range and the wider 5-95 Hz for comparison.
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